Empowering Digital Twin Development: A Profound Exploration with Altair Tools

Digital Twin (DT) Technology is fast becoming an integral part of various industries. Companies looking to transform their businesses are embracing it at a faster pace since this innovative tool can quickly detect and solve physical issues, design and build better products, enhance operational efficiency and infuse greater profits. The driving force behind this technology is its ability to create or replicate real-time digital representation of physical assets by deploying smart sensors, which collect data resulting in better interpretation, judgement, forecast and improved product performance. This paper deals with developing Digital Twin for a DC motor that actuates serial arm manipulators, incorporating Altair tools such as Altair Inspire, Compose, Activate, Panopticon and HyperWorks. This project focused on creating a Digital Twin based on a DC motor's thermal model, that can accurately predict its temperature, the amount of current it holds, and its expected behavioural patterns in a given situation. In order to achieve real-time object detection, our research involved deploying YOLO V5s model on a Raspberry Pi, specifically targeting perfume, medicine, and juice bottles in domestic settings. The key result of this research is the Reduced Order Model Artificial Intelligence (ROMAI) model, which accurately predicts the amount of current in a base motor, and its temperature, using data thrown up by current and temperature sensors. Thus, it can be safely concluded that the ROMAI Model serves as a critical component in ensuring Digital Twin's operational reliability, equipping it better to respond effectively to the DC motor's changing conditions.